Human Protein Atlas Database
Google DeepMindOfficialPublished by Google DeepMind
- Skill
- Tool integration
- Science and research
- Apache-2.0
- Low risk
- Has scripts
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
The Human Protein Atlas (HPA) database integration allows researchers to access semi-quantitative protein expression data derived from immunohistochemistry (IHC) across normal human tissues and cancer types. This tool bridges the gap between transcriptomic data and actual protein presence, providing insights into subcellular localization and systemic protein abundance. It requires the use of a provided CLI wrapper to ensure fair usage and proper data handling.
What it does
- Maps standard gene symbols to Ensembl IDs for database queries.
- Retrieves semi-quantitative protein expression levels in specific human tissues.
- Identifies subcellular organelles and structures where a protein is localized.
- Fetches comprehensive metadata entries for specific genes.
- Performs advanced searches based on expression criteria and specificity categories.
When to use it
- When you need to confirm if a protein product exists for a specific gene.
- When investigating the subcellular localization of a protein within a cell.
- When comparing RNA-seq consensus data with actual protein expression levels.
- When filtering for genes based on tissue-specific or brain-region-specific expression patterns.
Advantages
- Provides protein-level validation beyond simple mRNA transcription data.
- Standardizes queries through a robust CLI wrapper.
- Supports complex filtering via the HPA search query syntax.
- Automates the retrieval of large-scale biological datasets into JSON format.
Usage examples
- “Get the Ensembl ID for the TP53 gene.”
- The agent executes the resolve-ensembl-id command to map the symbol to its corresponding Ensembl ID, saving the result to a JSON file.
- “Where is the protein encoded by ENSG00000141736 located within the cell?”
- The agent uses the get-subcellular-location command to retrieve and report the specific organelles or structures associated with the protein.
- “Find genes that are enriched in the amygdala.”
- The agent uses the search-hpa command with the query 'brain_category_rna:amygdala' to return a list of matching genes.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/google-deepmind/human-protein-atlas-database/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip human-protein-atlas-database.zip -d .agents/skills
Documentation by Google DeepMind
Human Protein Atlas (HPA) Database Integration
This skill provides semi-quantitative protein expression and spatial localisation data from the Human Protein Atlas (HPA). While RNA-seq (e.g., GTEx) tells us if a gene is being transcribed, HPA confirms if the protein product actually exists, where it is located within the cell (e.g. nucleus vs cytoplasm), and its concentration in systemic blood circulation. The data is based on Immunohistochemistry (IHC) across normal human tissues and cancer types.
Prerequisites
uv: Read theuvskill and follow its Setup instructions to ensureuvis installed and on PATH.User Notification: If .licenses/human_protein_atlas_database_LICENSE.txt does not already exist in the workspace root directory then (1) prominently notify the user to check the terms at https://www.proteinatlas.org/about/licence, then (2) create the file recording the notification text and timestamp.
When to Use
Use this skill when you need to:
Map a gene symbol to its Ensembl ID for HPA queries.
Retrieve the semi-quantitative protein abundance in normal human tissues and cancer types based on IHC staining (High, Medium, Low, or Not Detected).
Find the specific organelles or subcellular structures where a protein has been localized (e.g., nucleoplasm, mitochondria).
Check the consistency/agreement between RNA-seq consensus and protein expression levels.
Search for genes based on specific protein expression criteria (e.g., "elevated in amygdala" or "secreted proteins").
Do NOT use when you need to:
Query eQTLs, pQTLs, or any variant-level associations. HPA provides wild-type expression data and knows nothing about QTLs.
Query gene expression in non-human species. HPA is strictly for human proteins.
Retrieve purely quantitative RNA expression without interest in the protein product (consider using the GTEx skill instead).
Command Selection Guide
Pick the right command on the first try. Match the user's input to the correct subcommand below.
Map a gene symbol to Ensembl ID:
resolve-ensembl-idGet tissue protein expression levels:
get-tissue-expressionGet subcellular location of a protein:
get-subcellular-locationGet the full HPA metadata entry for a gene:
get-atlas-entrySearch HPA for genes matching specific criteria:
search-hpa
Quick Start
# Map the ERBB2 gene symbol to its Ensembl ID
uv run scripts/hpa_cli.py resolve-ensembl-id ERBB2 --output /tmp/erbb2_id.json
# Get subcellular location by Ensembl ID
uv run scripts/hpa_cli.py get-subcellular-location ENSG00000141736 --output /tmp/erbb2_location.jsonAll subcommands write JSON to disk. Always save output in the /tmp/ directory.
The default output file is /tmp/hpa_output.json if --output is not
specified.
Commands
1. resolve-ensembl-id — Gene Symbol → Ensembl ID
Maps a common gene symbol (e.g., "TP53", "ERBB2") to its Ensembl gene ID. HPA endpoints are strictly Ensembl-based.
uv run scripts/hpa_cli.py resolve-ensembl-id TP53 --output /tmp/tp53_id.jsonArguments:
gene_symbol(positional): The standard gene symbol (e.g., "TP53").--output: Output file path (default:/tmp/hpa_output.json).
2. get-tissue-expression — Get Tissue Protein Levels
Returns a list of tissues and their corresponding protein expression levels (High, Medium, Low, or Not Detected) based on IHC staining.
uv run scripts/hpa_cli.py get-tissue-expression ENSG00000130234 \
--tissues "duodenum,thyroid gland" --output /tmp/tissue_expr.jsonArguments:
ensembl_id(positional): The Ensembl Gene ID.--tissues: Comma-separated list of tissues to filter by (optional, defaults to all available tissues).--output: Output file path (default:/tmp/hpa_output.json).
3. get-subcellular-location — Get Subcellular Location
Retrieves the specific organelles or cellular structures where the protein has been localized.
uv run scripts/hpa_cli.py get-subcellular-location ENSG00000141736 \
--output /tmp/subcellular.jsonArguments:
ensembl_id(positional): The Ensembl Gene ID.--output: Output file path.
4. get-atlas-entry — Get Full HPA Entry
Fetches the full metadata for a gene, including IHC scores, RNA-seq consensus, and subcellular location.
uv run scripts/hpa_cli.py get-atlas-entry ENSG00000254647 \
--output /tmp/ins_entry.jsonArguments:
ensembl_id(positional): The Ensembl Gene ID.--format: Format of the returned entry, e.g., json (default:json).--output: Output file path.
5. search-hpa — Search by Attribute
Allows filtering for genes based on specific criteria (e.g., "elevated in amygdala").
uv run scripts/hpa_cli.py search-hpa \
--query "brain_category_rna:amygdala" \
--output /tmp/search_results.jsonArguments:
--query: The search query string. Refer to references/search-api.md for details.--output: Output file path.
Core Rules
Use the Wrapper: ALWAYS execute the provided helper scripts to query the database rather than accessing the database directly. The scripts automatically enforce fair use and implement retry logic.
Notification: If this skill is used, ensure this is mentioned in the output.
API Versioning
The HPA website at www.proteinatlas.org always serves the latest data
release. Older archived versions can be accessed via vNN.proteinatlas.org
(e.g., v24.proteinatlas.org), while the current version's subdomain redirects
to www.proteinatlas.org. This skill's scripts query the latest version by
default.
Common Errors
If no results are returned, confirm the query is detailed enough starting with the api reference in references/search-api.md
If you cannot find the results, search the web for example HPA queries and use these to construct a better query.
The output is usually large. Use jq or write your own python data parsing library to process the search results. Never output to stdout, or cat the output file.
Files
- ATTRIBUTION.mdAdded by AutoGineer938 B
- LICENSELicence file added by AutoGineer11.1 KiB
- SKILL.md6.3 KiB
- references/citation.bib2.7 KiB
- references/search-api.md5.8 KiB
- scripts/hpa_cli.pyScript8.1 KiB
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Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs Python
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Attribution and licence
- Published by
- Google DeepMind
- Publisher
- Google DeepMind (Official)
- Source
- google-deepmind/science-skills · skills/human_protein_atlas_database · commit 4de6786
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
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